A Non-Technical Guide to How Inkeep Differs from ChatGPT

The question "Can't we just use ChatGPT?" is something we hear a lot during our calls with non-engineering buyers. So this blog is a direct shot to bring clarity to this question.

In short, ChatGPT is a personal AI assistant with generalist knowledge limited to OpenAI models like GPT-4. Teams can use ChatGPT for generalist tasks like writing, brainstorming, searching the internet, and creating custom conversational AI assistants called Custom GPTs that ground ChatGPT into a specific knowledge base. Because of this, ChatGPT optimizes internal employee productivity—helping teams write faster, research better, and find information across Slack, Google Drive, and SharePoint.

Inkeep is a framework to create teams of specialist AI Agents (multi-agent AI) that can autonomously use tools (like your business apps) in a manner that's grounded with your company knowledge base (via RAG) to drive certain outcomes that get work done. Teams can create and manage Agents in a single platform, and easily ship Agents as ready-to-deploy chat assistants, workflows, and even Slack apps.

The distinction matters because these platforms solve fundamentally different problems:

• ChatGPT optimizes internal employee productivity—helping teams write faster, research better, and find information across Slack, Google Drive, and SharePoint
• Inkeep enables employees to automate their own work with AI Agents, which can be thought of as AI Employees

This guide will help you understand which platform fits your requirements, when each solution excels, and why many enterprises need both—but for entirely different use cases.

Head-to-Head Comparison

What is ChatGPT?

ChatGPT (for Team and Enterprise) is a SaaS productivity tool that provides employees with conversational AI capabilities, customizable workflow templates, and company knowledge search across connected applications.

Its core capabilities include:

• Custom GPTs enable teams to create no-code AI assistants tailored to specific departmental workflows. HR teams can build GPTs for employee onboarding, finance teams for expense policy questions, and marketing teams for content brainstorming.
• Company Knowledge is ChatGPT's enterprise search feature that connects Google Drive, SharePoint, Notion and more to make company information searchable through conversational AI. When employees ask questions, ChatGPT searches across these connected apps and provides answers with citations to source documents.
• File Analysis supports uploading up to 20 files per project for Q&A, data analysis, and document processing. This works well for analyzing spreadsheets, reviewing contracts, or extracting insights from presentations.
• Admin Controls provide workspace management, sharing permissions, and governance over which third-party GPTs employees can access. IT teams can manage who sees what and maintain some level of security control.

Target Use Cases

ChatGPT Team and Enterprise excel at:

1. Employee productivity enhancement - Writing emails, creating content, brainstorming ideas
2. Internal knowledge retrieval - Searching across Slack conversations, Google Drive documents, SharePoint files
3. Departmental workflows - HR onboarding materials, finance policy questions
4. Data analysis - Quick insights from uploaded spreadsheets and reports
5. Research acceleration - Summarizing long documents, comparing information sources

What ChatGPT Is NOT

• ChatGPT is not embeddable in customer-facing channels
• It does not coordinate multiple specialized agents
• Cannot write to external systems
• Fundamentally architected as an employee-accessed web interface rather than a customer-facing platform

What is Inkeep?

Inkeep is an advanced multi-agent AI framework that enables companies to build, deploy, and govern AI agent systems. Each AI Agent works within a team of other specialist AI Agents, where each Agent can have its own prompt, pre-defined goal, access to model (GPT, Claude, etc) with its own context window, self-updating knowledge infrastructure, and omnichannel deployment capabilities. These Agents can be embedded in conversational format and can be embedded anywhere like:

• Chat Bubble for your websites
• In-App Copilot for your product
• Copilot for your support platform
• Slack, Teams, or Discord search and chat-assistant
• A tool that can be used in Claude, ChatGPT, and Cursor

Core Capabilities

• Autonomous Multi-Agent Orchestration enables multiple specialized AI agents to collaborate on their own. Unlike simple linear chains or single-assistant approaches, Inkeep's graph architecture mirrors real-world team dynamics where specialized agents handle specific domains and coordinate complex workflows.
• Self-Updating Knowledge Base automatically ingests and maintains current information from websites, Notion, Confluence, APIs, databases, and documentation sources. Context Fetchers with template interpolation ({{variable}} syntax) enable dynamic data injection, going beyond static prompts to real-time information integration for complex data requirements.
• Omni-channel Deployment allows AI agents to operate wherever customers and employees interact: embedded web chat, Slack, Discord servers, Zendesk support tickets, Salesforce CRM, in-product experiences, and mobile applications. This deployment flexibility is architected into the platform rather than requiring custom development.
• Visual Builder + TypeScript SDK bridges the gap between technical and non-technical users. Business stakeholders can design agent systems visually, then developers can extend with custom code. The platform auto-converts visual configurations to TypeScript code via inkeep pull, and conversely, developers can build complex orchestrations that generate visual representations for stakeholders via inkeep push.
• Compliance-Ready Source Attribution System provides automatic chains of evidence for every interaction through Artifact Components. This compliance-ready feature tracks information sources, provides citations, and maintains audit trails. This is critical for enterprise deployments where attribution and accountability matter.
• Enterprise Governance includes agent-level credential management, confidence-gated automation (only answer when certain), multi-tenancy with secure isolation, and comprehensive OpenTelemetry tracing for observability.

Target Use Cases

Inkeep is purpose-built for many CX, GTM and Ops use-cases. Some of our favorites include:

1. Competitive intelligence - Inkeep Agents can autonomously research competitors, provide comparative product insight, draft battle cards for sales, and content for marketing
2. Customer support automation - AI agents deflecting tickets across web, Slack, Zendesk with 70-80% success rates
3. Product onboarding and activation - In-product guidance increasing API activation rates by 18%
4. Documentation assistance - Self-updating knowledge systems with source citations for compliance
5. Community support - Auto-resolving forum questions while maintaining brand voice and confidence thresholds

Why Use Multi-Agents Over General LLMs Like ChatGPT?

You might be asking, what's the point of a multi-agent system when I already have my own LLM like ChatGPT? This is a fair question, especially as a single large language model (LLM) is powerful.

The answer lies in the limitations observed when a single LLM or AI agent is given too many responsibilities, namely in the form of tool overload & context management:

• For tool overload, performance degrades when a single agent is equipped with a large number of tools (e.g., google calendar, searching documents, etc.). With too many options, the agent may struggle to choose the correct tool for a given task. This leads to lower accuracy. By creating specialized agents with only one or two tools each, you simplify the decision-making process and increase reliability.
• For context management, a single agent can also be confused by conflicting instructions. For instance, an agent might be told to "be brief and concise" but also to "be warm, empathetic, and professional." These goals can be at odds. A multi-agent approach resolves this by assigning different facets of a complex task to different agents, each with a simple, unambiguous set of instructions.

By breaking down a complex workload into smaller, specialized tasks, multi-agent systems become a best practice for building sophisticated and scalable AI. Inkeep does this exceptionally well.

You can read more about this in our blog on what is a multi-agent system.

5 Critical Differences That Matter Most

1 — Tool vs Platform

ChatGPT is an internal tool that employees access by logging into chat.openai.com. It's designed for the same use case as Microsoft Copilot or Google Workspace AI—helping employees be more productive in their daily work.

Inkeep is a platform for building AI systems that customers and employees interact with inside the channels they already use. The AI isn't accessed through a separate login; it's embedded in your website, Slack community, Zendesk tickets, and product interface.

Why This Matters: If you're evaluating AI for CX, GTM or Ops use-cases like customer support deflection, product onboarding, or community assistance, ChatGPT fundamentally cannot deploy to these customer-facing channels. You would need to build a custom integration using ChatGPT's API, essentially rebuilding Inkeep from scratch.

2 — Model Choice and Single Assistant vs Multi-Agents

ChatGPT: One assistant per conversation. Custom GPTs exist but can't communicate with each other or hand off tasks. Locked into OpenAI ecosystem with web interface and read-only integrations. Built for employee productivity, not customer-facing deployments.

Inkeep: Graph-based multi-agent orchestration where specialized agents collaborate through handoffs (permanent transfer) and delegation (temporary task assignment). Works with OpenAI models plus Claude, Gemini, and custom models. Enterprise platform with omnichannel deployment and bidirectional system actions.

Why it Matters: Real workflows require multiple specialists working together—like customer support needing triage agents, product experts, billing specialists, and technical troubleshooters. ChatGPT's single-assistant architecture can't model these workflows or deploy to customer channels. Even if you prefer OpenAI models, you need an enterprise platform like Inkeep to build customer-facing AI systems that coordinate agents and take actions in your systems.

3 — Manual Knowledge vs Self-Updating Systems

ChatGPT requires manual file uploads (up to 20 files per project) or read-only connections to apps like Slack and Google Drive. When your documentation changes, someone must manually update the knowledge base or wait for sync cycles.

Inkeep automatically crawls and ingests content from websites, Notion, Confluence, APIs, and databases with template interpolation for dynamic data. When documentation updates, knowledge bases refresh automatically without manual intervention.

Why This Matters: For companies with frequently changing documentation (weekly releases, constant product updates, evolving API specs), manual knowledge management doesn't scale. Inkeep's self-updating infrastructure ensures AI agents always have current information.

4 — Read-Only vs Bidirectional Actions

ChatGPT can search and retrieve information from connected apps but cannot write back to them. Company Knowledge searches Slack and Google Drive, but ChatGPT cannot create tickets, update CRM records, or trigger workflows.

Inkeep supports bidirectional integrations through MCP (Model Context Protocol) and APIs, enabling agents to:

• Create and update Zendesk tickets
• Write to Salesforce CRM fields
• Update database records
• Trigger workflow automations
• Post messages to Slack
• Call external APIs

Why This Matters: Modern AI systems need to take actions, not just provide information. When a customer support agent resolves an issue, it should create the ticket, update the customer record, and trigger follow-up workflows—not just suggest these actions to a human.

5 — Template Customization vs Programmatic Control

ChatGPT offers Custom GPTs—template-based customization without code. You can configure instructions, upload knowledge files, and set behaviors, but you cannot programmatically control agent logic, state management, or orchestration patterns.

Inkeep provides both a visual builder for non-technical users AND a full TypeScript SDK for developers. This dual development model enables:

• Business users designing agent workflows visually
• Developers extending with custom code for complex logic
• Bidirectional conversion (visual to code, code to visual)
• Programmatic control over agent relationships, context management, and orchestration

Why This Matters: Enterprise AI requirements evolve from simple templates to sophisticated systems. Inkeep grows with your needs—start visually, extend programmatically—without hitting architectural limitations.

When to Choose ChatGPT Team/Enterprise

ChatGPT is the right choice when your primary need is internal employee productivity enhancement. Consider ChatGPT when:

Ideal Use Cases for ChatGPT

1. General Employee Productivity Your team needs AI assistance for writing emails, creating content, brainstorming ideas, and general research tasks. ChatGPT excels at these productivity workflows with minimal setup.

2. Departmental Workflow Templates HR needs onboarding assistants, finance wants policy Q&A, marketing requires content brainstorming. Custom GPTs provide no-code templates for these specific workflows without requiring developer resources.

3. Quick Deployment with Minimal Setup You need AI capabilities deployed within 1-2 weeks with hosted infrastructure and no custom development. ChatGPT provides immediate value with minimal configuration.

4. Budget-Conscious Deployments for Small Teams Organizations with fewer than 50 employees find the $25-30 per user per month pricing competitive, especially compared to building custom AI solutions or licensing enterprise platforms.

5. Read-Only Knowledge Access Your requirement is purely search and retrieval—finding information, not taking actions. ChatGPT's read-only integrations suffice when you don't need AI to write to systems or trigger workflows.

When to Choose Inkeep

Inkeep is the right choice when you need to build sophisticated AI agent systems for customers or complex internal workflows that require orchestration, governance, and omnichannel deployment.

Ideal Use Cases for Inkeep

1. Customer-Facing AI Systems You need AI deployed to customer touchpoints: website chat, Slack communities, Discord servers, Zendesk tickets, in-product guidance, mobile apps. ChatGPT cannot embed in these channels—it's accessed only through chat.openai.com by employees.

2. Multi-Agent Coordination Your workflow requires specialized agents collaborating through handoffs and delegation. Examples:

• General support agent triages, then hands off to billing specialist or technical expert
• Documentation agent delegates code analysis to technical agent, receives results, continues helping customer
• Onboarding agent coordinates with product specialist and account manager for complex implementations

ChatGPT's single-assistant architecture cannot model these coordination patterns.

3. Internal Knowledge Retrieval Employees struggle to find information across Slack conversations, Google Drive documents, and SharePoint files. Inkeep's Ask AI & semantic search capabilities offer company Knowledge feature provides conversational search across these apps with citations.

4. Action Automation Requirements AI needs to DO things, not just suggest them:

• Create and update support tickets in Zendesk
• Write customer data to Salesforce CRM
• Trigger workflow automations in your systems
• Update database records based on conversations
• Post messages to customer Slack channels

ChatGPT's read-only integrations cannot take these actions.

5. Frequently Changing Documentation Your product documentation, API specs, or knowledge base updates weekly (or daily). Manual file uploads don't scale, and you need self-updating knowledge infrastructure that automatically stays current.

6. Compliance and Attribution Requirements Regulated industries or enterprise policies require:

• Source citations for every AI response
• Audit trails showing information sources
• Agent-level credential management
• Confidence-gated responses (only answer when certain)
• Granular governance and tracing

7. Developer Customization Needs You need programmatic control over agent logic, state management, custom integrations, and orchestration patterns. Template-based customization (Custom GPTs) hits limitations, and you need a full TypeScript SDK.

8. Omnichannel Deployment at Scale AI must work consistently across:

• Web embedded chat
• Slack communities and Discord servers
• Zendesk support tickets
• Salesforce CRM interactions
• In-product guidance and tooltips
• Mobile applications

Building custom deployment infrastructure for each channel doesn't make sense when Inkeep provides this out-of-box.

9. Production-Scale Enterprise Deployments Organizations with 100+ employees, dedicated AI/product engineering teams, and requirements for 70-80% ticket deflection rates need enterprise-grade platforms, not productivity tools.

"Can't we just embed a Custom GPT on our website?"

Custom GPTs are accessed through chat.openai.com—they're not embeddable UI components. To build what you're describing, you would need to:

1. Build a custom chat interface from scratch
2. Integrate ChatGPT API for conversational AI
3. Implement knowledge base management and vector stores
4. Deploy to multiple channels (Slack, Zendesk, mobile)
5. Build source attribution and artifact tracking system
6. Develop multi-agent orchestration logic
7. Create governance and compliance infrastructure

At that point, you're building Inkeep from scratch using ChatGPT as a language model. The question isn't "ChatGPT or Inkeep"—it's "build an AI platform from scratch or use an enterprise framework."

Inkeep provides all this out-of-box: visual builder, UI kit, RAG infrastructure, multi-agent orchestration, omnichannel deployment, and enterprise governance.

Decision Framework: Which Should You Choose?

ChatGPT and Inkeep are not mutually exclusive. Many companies use both. Below is a framework to determine which solution fits your requirements:

Choose ChatGPT if:

• ✅ Internal use only - No customer-facing requirements
• ✅ Employee productivity focus - Writing, research, data analysis
• ✅ Single-assistant simplicity - No need for multiple agents coordinating
• ✅ Read-only knowledge - Just searching internal documents, not taking actions
• ✅ Quick deployment - Need AI capabilities within 1-2 weeks
• ✅ Infrequent knowledge updates - Documentation changes quarterly, manual uploads manageable

Choose Inkeep if:

• ✅ Customer-facing AI & Internal AI - Deploy to website, Slack communities, Zendesk, in-product
• ✅ Multi-agent coordination - Specialized agents need to hand off or delegate tasks
  • An example would be in Blog Content generation: You can have separate agents autonomously work together where one researches, another brainstorms ideas, another that writes the content, and an SEO checker. Doing all this autonomously in ChatGPT is simply not possible and risks hallucination and context overload.
• ✅ Action automation - AI must create tickets, update CRM, trigger workflows
• ✅ Omnichannel deployment - AI needs to work across multiple customer touchpoints
• ✅ Frequent knowledge changes - Documentation updates weekly or daily
• ✅ Developer customization - Need programmatic control with TypeScript SDK
• ✅ Compliance requirements - Source attribution, audit trails, agent-level governance
• ✅ Enterprise scale - 100+ employees with dedicated AI/product engineering teams

You Might Need Both if:

• ✅ General employee assistance (ChatGPT) AND specialized domain workflows (Inkeep)
• ✅ Quick wins for employees (ChatGPT) AND strategic AI transformation (Inkeep)

Many enterprises deploy ChatGPT for employee productivity while building customer-facing AI systems on Inkeep. They're complementary tools serving different purposes.